Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Liangqiong Qu"'
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-5 (2023)
Abstract Brain magnetic resonance imaging (MRI) provides detailed soft tissue contrasts that are critical for disease diagnosis and neuroscience research. Higher MRI resolution typically comes at the cost of signal-to-noise ratio (SNR) and tissue con
Externí odkaz:
https://doaj.org/article/28e700fb847745baa2c071aa90bc338f
Publikováno v:
IET Computer Vision, Vol 12, Iss 1, Pp 95-103 (2018)
Shadow features such as colour ratio, texture, and chromaticity have proved to be quite effective in shadow detection. Many shadow detection methods have been proposed on the basis of different features. However, previous works for shadow detection m
Externí odkaz:
https://doaj.org/article/2874ed315f734d968f719792ed4ae697
Autor:
Yan-Ran Wang, Pengcheng Wang, Lisa Christine Adams, Natasha Diba Sheybani, Liangqiong Qu, Amir Hossein Sarrami, Ashok Joseph Theruvath, Sergios Gatidis, Tina Ho, Quan Zhou, Allison Pribnow, Avnesh S. Thakor, Daniel Rubin, Heike E. Daldrup-Link
Publikováno v:
European Journal of Nuclear Medicine and Molecular Imaging. 50:1337-1350
Publikováno v:
IEEE Transactions on Multimedia. :1-14
Publikováno v:
Neurocomputing. 501:778-789
Publikováno v:
Medical Image Synthesis ISBN: 9781003243458
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0fc17beb4706357a0ecd8adb84a3120c
https://doi.org/10.1201/9781003243458-14
https://doi.org/10.1201/9781003243458-14
Autor:
Yan-Ran (Joyce) Wang, Liangqiong Qu, Natasha Diba Sheybani, Xiaolong Luo, Jiangshan Wang, Kristina Elizabeth Hawk, Ashok Joseph Theruvath, Sergios Gatidis, Xuerong Xiao, Allison Pribnow, Daniel Rubin, Heike E. Daldrup-Link
Publikováno v:
Radiol Artif Intell
PURPOSE: To develop a deep learning approach that enables ultra-low-dose, 1% of the standard clinical dosage (3 MBq/kg), ultrafast whole-body PET reconstruction in cancer imaging. MATERIALS AND METHODS: In this Health Insurance Portability and Accoun
Autor:
Feifei Wang, Fuqiang Ren, Zhuoran Ma, Liangqiong Qu, Ronan Gourgues, Chun Xu, Ani Baghdasaryan, Jiachen Li, Iman Esmaeil Zadeh, Johannes W. N. Los, Andreas Fognini, Jessie Qin-Dregely, Hongjie Dai
Publikováno v:
Nature Nanotechnology. 17:653-660
Light scattering by biological tissues sets a limit to the penetration depth of high-resolution optical microscopy imaging of live mammals in vivo. An effective approach to reduce light scattering and increase imaging depth is to extend the excitatio
Autor:
Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel Rubin, Lei Xing, Yuyin Zhou
The curation of large-scale medical datasets from multiple institutions necessary for training deep learning models is challenged by the difficulty in sharing patient data with privacy-preserving. Federated learning (FL), a paradigm that enables priv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f9c6a3bb928a8b95c35003866a2014f
http://arxiv.org/abs/2205.08576
http://arxiv.org/abs/2205.08576
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-12
Segmenting breast tumors from dynamic contrast-enhanced magnetic resonance (DCE-MR) images is a critical step for early detection and diagnosis of breast cancer. However, variable shapes and sizes of breast tumors, as well as inhomogeneous background